Overview

Dataset statistics

Number of variables34
Number of observations1135
Missing cells13259
Missing cells (%)34.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.6 KiB
Average record size in memory272.1 B

Variable types

Categorical11
Numeric8
Text4
Unsupported10
DateTime1

Alerts

Tax has constant value ""Constant
Discount details has constant value ""Constant
Service name has constant value ""Constant
Date is highly overall correlated with Payment Status and 1 other fieldsHigh correlation
Lineitem price is highly overall correlated with Service cityHigh correlation
Order number is highly overall correlated with Payment Status and 1 other fieldsHigh correlation
Payment Status is highly overall correlated with Date and 2 other fieldsHigh correlation
Service city is highly overall correlated with Lineitem priceHigh correlation
Service distance is highly overall correlated with Service feeHigh correlation
Service fee is highly overall correlated with Service distanceHigh correlation
Status is highly overall correlated with Date and 2 other fieldsHigh correlation
Subtotal is highly overall correlated with TotalHigh correlation
Total is highly overall correlated with SubtotalHigh correlation
Discount is highly imbalanced (78.4%)Imbalance
Service city is highly imbalanced (86.3%)Imbalance
Lineitem quantity is highly imbalanced (72.7%)Imbalance
Lineitem cost is highly imbalanced (81.7%)Imbalance
Discount details has 1053 (92.8%) missing valuesMissing
Service name has 16 (1.4%) missing valuesMissing
Service date has 1135 (100.0%) missing valuesMissing
Service time has 1135 (100.0%) missing valuesMissing
Service address has 16 (1.4%) missing valuesMissing
Service city has 822 (72.4%) missing valuesMissing
Service state has 1135 (100.0%) missing valuesMissing
Service postal has 1135 (100.0%) missing valuesMissing
Lineitem sku has 1135 (100.0%) missing valuesMissing
Lineitem option has 1135 (100.0%) missing valuesMissing
Lineitem note has 1135 (100.0%) missing valuesMissing
Customer email has 1135 (100.0%) missing valuesMissing
Remark has 1135 (100.0%) missing valuesMissing
Internal note has 1135 (100.0%) missing valuesMissing
Lineitem price is highly skewed (γ1 = 22.28396979)Skewed
Service date is an unsupported type, check if it needs cleaning or further analysisUnsupported
Service time is an unsupported type, check if it needs cleaning or further analysisUnsupported
Service state is an unsupported type, check if it needs cleaning or further analysisUnsupported
Service postal is an unsupported type, check if it needs cleaning or further analysisUnsupported
Lineitem sku is an unsupported type, check if it needs cleaning or further analysisUnsupported
Lineitem option is an unsupported type, check if it needs cleaning or further analysisUnsupported
Lineitem note is an unsupported type, check if it needs cleaning or further analysisUnsupported
Customer email is an unsupported type, check if it needs cleaning or further analysisUnsupported
Remark is an unsupported type, check if it needs cleaning or further analysisUnsupported
Internal note is an unsupported type, check if it needs cleaning or further analysisUnsupported
Service fee has 16 (1.4%) zerosZeros
Service distance has 210 (18.5%) zerosZeros

Reproduction

Analysis started2024-04-05 21:44:55.364955
Analysis finished2024-04-05 21:45:17.145765
Duration21.78 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
26 Mar 2024
209 
25 Mar 2024
129 
03 Apr 2024
123 
21 Mar 2024
97 
24 Mar 2024
88 
Other values (12)
489 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters12485
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row05 Apr 2024
2nd row05 Apr 2024
3rd row05 Apr 2024
4th row05 Apr 2024
5th row05 Apr 2024

Common Values

ValueCountFrequency (%)
26 Mar 2024 209
18.4%
25 Mar 2024 129
11.4%
03 Apr 2024 123
10.8%
21 Mar 2024 97
8.5%
24 Mar 2024 88
7.8%
02 Apr 2024 87
7.7%
31 Mar 2024 74
 
6.5%
28 Mar 2024 65
 
5.7%
22 Mar 2024 57
 
5.0%
27 Mar 2024 48
 
4.2%
Other values (7) 158
13.9%

Length

2024-04-05T21:45:17.388839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024 1135
33.3%
mar 866
25.4%
apr 269
 
7.9%
26 209
 
6.1%
25 129
 
3.8%
03 123
 
3.6%
21 97
 
2.8%
24 88
 
2.6%
02 87
 
2.6%
31 74
 
2.2%
Other values (10) 328
 
9.6%

Most occurring characters

ValueCountFrequency (%)
2 3205
25.7%
2270
18.2%
0 1446
11.6%
4 1233
 
9.9%
r 1135
 
9.1%
M 866
 
6.9%
a 866
 
6.9%
A 269
 
2.2%
p 269
 
2.2%
3 227
 
1.8%
Other values (6) 699
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3205
25.7%
2270
18.2%
0 1446
11.6%
4 1233
 
9.9%
r 1135
 
9.1%
M 866
 
6.9%
a 866
 
6.9%
A 269
 
2.2%
p 269
 
2.2%
3 227
 
1.8%
Other values (6) 699
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3205
25.7%
2270
18.2%
0 1446
11.6%
4 1233
 
9.9%
r 1135
 
9.1%
M 866
 
6.9%
a 866
 
6.9%
A 269
 
2.2%
p 269
 
2.2%
3 227
 
1.8%
Other values (6) 699
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3205
25.7%
2270
18.2%
0 1446
11.6%
4 1233
 
9.9%
r 1135
 
9.1%
M 866
 
6.9%
a 866
 
6.9%
A 269
 
2.2%
p 269
 
2.2%
3 227
 
1.8%
Other values (6) 699
 
5.6%

Order number
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.867841
Minimum10
Maximum117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:17.831101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile29
Q143
median66
Q389
95-th percentile111
Maximum117
Range107
Interquartile range (IQR)46

Descriptive statistics

Standard deviation27.359843
Coefficient of variation (CV)0.40916294
Kurtosis-1.1090733
Mean66.867841
Median Absolute Deviation (MAD)23
Skewness0.093206376
Sum75895
Variance748.561
MonotonicityDecreasing
2024-04-05T21:45:18.275938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 38
 
3.3%
79 32
 
2.8%
30 29
 
2.6%
39 28
 
2.5%
98 28
 
2.5%
71 28
 
2.5%
66 27
 
2.4%
111 26
 
2.3%
42 24
 
2.1%
48 24
 
2.1%
Other values (76) 851
75.0%
ValueCountFrequency (%)
10 1
 
0.1%
18 9
 
0.8%
19 4
 
0.4%
20 18
1.6%
21 11
 
1.0%
23 8
 
0.7%
27 5
 
0.4%
29 38
3.3%
30 29
2.6%
31 11
 
1.0%
ValueCountFrequency (%)
117 19
1.7%
115 8
 
0.7%
114 1
 
0.1%
113 1
 
0.1%
112 15
1.3%
111 26
2.3%
110 9
 
0.8%
109 21
1.9%
108 8
 
0.7%
106 13
1.1%
Distinct86
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:18.907038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.1559471
Min length7

Characters and Unicode

Total characters8122
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.4%

Sample

1st row#1172024
2nd row#1172024
3rd row#1172024
4th row#1172024
5th row#1172024
ValueCountFrequency (%)
292024 38
 
3.3%
792024 32
 
2.8%
302024 29
 
2.6%
392024 28
 
2.5%
982024 28
 
2.5%
712024 28
 
2.5%
662024 27
 
2.4%
1112024 26
 
2.3%
422024 24
 
2.1%
482024 24
 
2.1%
Other values (76) 851
75.0%
2024-04-05T21:45:19.826749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2445
30.1%
4 1383
17.0%
0 1356
16.7%
# 1135
14.0%
1 418
 
5.1%
6 284
 
3.5%
9 272
 
3.3%
3 249
 
3.1%
7 213
 
2.6%
8 205
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2445
30.1%
4 1383
17.0%
0 1356
16.7%
# 1135
14.0%
1 418
 
5.1%
6 284
 
3.5%
9 272
 
3.3%
3 249
 
3.1%
7 213
 
2.6%
8 205
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2445
30.1%
4 1383
17.0%
0 1356
16.7%
# 1135
14.0%
1 418
 
5.1%
6 284
 
3.5%
9 272
 
3.3%
3 249
 
3.1%
7 213
 
2.6%
8 205
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2445
30.1%
4 1383
17.0%
0 1356
16.7%
# 1135
14.0%
1 418
 
5.1%
6 284
 
3.5%
9 272
 
3.3%
3 249
 
3.1%
7 213
 
2.6%
8 205
 
2.5%

Status
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Fulfilled
504 
Pending
347 
Confirmed
206 
Cancelled
78 

Length

Max length9
Median length9
Mean length8.3885463
Min length7

Characters and Unicode

Total characters9521
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPending
2nd rowPending
3rd rowPending
4th rowPending
5th rowPending

Common Values

ValueCountFrequency (%)
Fulfilled 504
44.4%
Pending 347
30.6%
Confirmed 206
18.1%
Cancelled 78
 
6.9%

Length

2024-04-05T21:45:20.148757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-05T21:45:20.442346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fulfilled 504
44.4%
pending 347
30.6%
confirmed 206
18.1%
cancelled 78
 
6.9%

Most occurring characters

ValueCountFrequency (%)
l 1668
17.5%
e 1213
12.7%
d 1135
11.9%
i 1057
11.1%
n 978
10.3%
f 710
7.5%
F 504
 
5.3%
u 504
 
5.3%
P 347
 
3.6%
g 347
 
3.6%
Other values (6) 1058
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1668
17.5%
e 1213
12.7%
d 1135
11.9%
i 1057
11.1%
n 978
10.3%
f 710
7.5%
F 504
 
5.3%
u 504
 
5.3%
P 347
 
3.6%
g 347
 
3.6%
Other values (6) 1058
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1668
17.5%
e 1213
12.7%
d 1135
11.9%
i 1057
11.1%
n 978
10.3%
f 710
7.5%
F 504
 
5.3%
u 504
 
5.3%
P 347
 
3.6%
g 347
 
3.6%
Other values (6) 1058
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1668
17.5%
e 1213
12.7%
d 1135
11.9%
i 1057
11.1%
n 978
10.3%
f 710
7.5%
F 504
 
5.3%
u 504
 
5.3%
P 347
 
3.6%
g 347
 
3.6%
Other values (6) 1058
11.1%

Payment Status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Paid
670 
Unpaid
465 

Length

Max length6
Median length4
Mean length4.8193833
Min length4

Characters and Unicode

Total characters5470
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnpaid
2nd rowUnpaid
3rd rowUnpaid
4th rowUnpaid
5th rowUnpaid

Common Values

ValueCountFrequency (%)
Paid 670
59.0%
Unpaid 465
41.0%

Length

2024-04-05T21:45:20.690841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-05T21:45:20.940693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
paid 670
59.0%
unpaid 465
41.0%

Most occurring characters

ValueCountFrequency (%)
a 1135
20.7%
i 1135
20.7%
d 1135
20.7%
P 670
12.2%
U 465
8.5%
n 465
8.5%
p 465
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1135
20.7%
i 1135
20.7%
d 1135
20.7%
P 670
12.2%
U 465
8.5%
n 465
8.5%
p 465
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1135
20.7%
i 1135
20.7%
d 1135
20.7%
P 670
12.2%
U 465
8.5%
n 465
8.5%
p 465
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1135
20.7%
i 1135
20.7%
d 1135
20.7%
P 670
12.2%
U 465
8.5%
n 465
8.5%
p 465
8.5%

Subtotal
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58870.131
Minimum3250
Maximum116850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:21.179276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3250
5-th percentile20900
Q145027.5
median57130
Q375240
95-th percentile101286.5
Maximum116850
Range113600
Interquartile range (IQR)30212.5

Descriptive statistics

Standard deviation23884.627
Coefficient of variation (CV)0.40571725
Kurtosis-0.4472597
Mean58870.131
Median Absolute Deviation (MAD)18070
Skewness0.1057331
Sum66817598
Variance5.7047543 × 108
MonotonicityNot monotonic
2024-04-05T21:45:21.486748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65890 42
 
3.7%
101030 38
 
3.3%
87641.66 32
 
2.8%
107886 29
 
2.6%
82496 28
 
2.5%
99810 28
 
2.5%
76320 28
 
2.5%
101885 27
 
2.4%
69420 26
 
2.3%
80815 24
 
2.1%
Other values (74) 833
73.4%
ValueCountFrequency (%)
3250 1
 
0.1%
4600 2
0.2%
5850 2
0.2%
5950 2
0.2%
7450 2
0.2%
8750 2
0.2%
11500 4
0.4%
11600 4
0.4%
12280 4
0.4%
12350 2
0.2%
ValueCountFrequency (%)
116850 1
 
0.1%
107886 29
2.6%
101885 27
2.4%
101030 38
3.3%
99810 28
2.5%
87641.66 32
2.8%
82496 28
2.5%
80815 24
2.1%
78900 24
2.1%
76320 28
2.5%

Tax
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
0
1135 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1135
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1135
100.0%

Length

2024-04-05T21:45:21.751641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-05T21:45:21.991496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1135
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1135
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1135
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1135
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1135
100.0%

Total
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58870.131
Minimum3250
Maximum116850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:22.211080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3250
5-th percentile20900
Q145027.5
median57130
Q375240
95-th percentile101286.5
Maximum116850
Range113600
Interquartile range (IQR)30212.5

Descriptive statistics

Standard deviation23884.627
Coefficient of variation (CV)0.40571725
Kurtosis-0.4472597
Mean58870.131
Median Absolute Deviation (MAD)18070
Skewness0.1057331
Sum66817598
Variance5.7047543 × 108
MonotonicityNot monotonic
2024-04-05T21:45:22.524300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65890 42
 
3.7%
101030 38
 
3.3%
87641.66 32
 
2.8%
107886 29
 
2.6%
82496 28
 
2.5%
99810 28
 
2.5%
76320 28
 
2.5%
101885 27
 
2.4%
69420 26
 
2.3%
80815 24
 
2.1%
Other values (74) 833
73.4%
ValueCountFrequency (%)
3250 1
 
0.1%
4600 2
0.2%
5850 2
0.2%
5950 2
0.2%
7450 2
0.2%
8750 2
0.2%
11500 4
0.4%
11600 4
0.4%
12280 4
0.4%
12350 2
0.2%
ValueCountFrequency (%)
116850 1
 
0.1%
107886 29
2.6%
101885 27
2.4%
101030 38
3.3%
99810 28
2.5%
87641.66 32
2.8%
82496 28
2.5%
80815 24
2.1%
78900 24
2.1%
76320 28
2.5%

Discount
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
0
1053 
2500
 
42
2750
 
16
3000
 
13
2000
 
11

Length

Max length4
Median length1
Mean length1.2167401
Min length1

Characters and Unicode

Total characters1381
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1053
92.8%
2500 42
 
3.7%
2750 16
 
1.4%
3000 13
 
1.1%
2000 11
 
1.0%

Length

2024-04-05T21:45:22.810394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-05T21:45:23.067439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1053
92.8%
2500 42
 
3.7%
2750 16
 
1.4%
3000 13
 
1.1%
2000 11
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1225
88.7%
2 69
 
5.0%
5 58
 
4.2%
7 16
 
1.2%
3 13
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1225
88.7%
2 69
 
5.0%
5 58
 
4.2%
7 16
 
1.2%
3 13
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1225
88.7%
2 69
 
5.0%
5 58
 
4.2%
7 16
 
1.2%
3 13
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1225
88.7%
2 69
 
5.0%
5 58
 
4.2%
7 16
 
1.2%
3 13
 
0.9%

Discount details
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)1.2%
Missing1053
Missing (%)92.8%
Memory size9.0 KiB
taxa de entrega (OVAPYA01)
82 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters2132
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtaxa de entrega (OVAPYA01)
2nd rowtaxa de entrega (OVAPYA01)
3rd rowtaxa de entrega (OVAPYA01)
4th rowtaxa de entrega (OVAPYA01)
5th rowtaxa de entrega (OVAPYA01)

Common Values

ValueCountFrequency (%)
taxa de entrega (OVAPYA01) 82
 
7.2%
(Missing) 1053
92.8%

Length

2024-04-05T21:45:23.279649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-05T21:45:23.531487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
taxa 82
25.0%
de 82
25.0%
entrega 82
25.0%
ovapya01 82
25.0%

Most occurring characters

ValueCountFrequency (%)
246
 
11.5%
e 246
 
11.5%
a 246
 
11.5%
t 164
 
7.7%
A 164
 
7.7%
V 82
 
3.8%
1 82
 
3.8%
0 82
 
3.8%
Y 82
 
3.8%
P 82
 
3.8%
Other values (8) 656
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
246
 
11.5%
e 246
 
11.5%
a 246
 
11.5%
t 164
 
7.7%
A 164
 
7.7%
V 82
 
3.8%
1 82
 
3.8%
0 82
 
3.8%
Y 82
 
3.8%
P 82
 
3.8%
Other values (8) 656
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
246
 
11.5%
e 246
 
11.5%
a 246
 
11.5%
t 164
 
7.7%
A 164
 
7.7%
V 82
 
3.8%
1 82
 
3.8%
0 82
 
3.8%
Y 82
 
3.8%
P 82
 
3.8%
Other values (8) 656
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
246
 
11.5%
e 246
 
11.5%
a 246
 
11.5%
t 164
 
7.7%
A 164
 
7.7%
V 82
 
3.8%
1 82
 
3.8%
0 82
 
3.8%
Y 82
 
3.8%
P 82
 
3.8%
Other values (8) 656
30.8%

Service name
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing16
Missing (%)1.4%
Memory size9.0 KiB
Taxa de entrega
1119 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters16785
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTaxa de entrega
2nd rowTaxa de entrega
3rd rowTaxa de entrega
4th rowTaxa de entrega
5th rowTaxa de entrega

Common Values

ValueCountFrequency (%)
Taxa de entrega 1119
98.6%
(Missing) 16
 
1.4%

Length

2024-04-05T21:45:23.736220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-05T21:45:23.960480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
taxa 1119
33.3%
de 1119
33.3%
entrega 1119
33.3%

Most occurring characters

ValueCountFrequency (%)
a 3357
20.0%
e 3357
20.0%
2238
13.3%
T 1119
 
6.7%
x 1119
 
6.7%
d 1119
 
6.7%
n 1119
 
6.7%
t 1119
 
6.7%
r 1119
 
6.7%
g 1119
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3357
20.0%
e 3357
20.0%
2238
13.3%
T 1119
 
6.7%
x 1119
 
6.7%
d 1119
 
6.7%
n 1119
 
6.7%
t 1119
 
6.7%
r 1119
 
6.7%
g 1119
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3357
20.0%
e 3357
20.0%
2238
13.3%
T 1119
 
6.7%
x 1119
 
6.7%
d 1119
 
6.7%
n 1119
 
6.7%
t 1119
 
6.7%
r 1119
 
6.7%
g 1119
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3357
20.0%
e 3357
20.0%
2238
13.3%
T 1119
 
6.7%
x 1119
 
6.7%
d 1119
 
6.7%
n 1119
 
6.7%
t 1119
 
6.7%
r 1119
 
6.7%
g 1119
 
6.7%

Service date
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1135
Missing (%)100.0%
Memory size9.0 KiB

Service time
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1135
Missing (%)100.0%
Memory size9.0 KiB

Service fee
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2521.3656
Minimum0
Maximum3500
Zeros16
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:24.130506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2000
Q12000
median2750
Q32750
95-th percentile3000
Maximum3500
Range3500
Interquartile range (IQR)750

Descriptive statistics

Standard deviation544.97414
Coefficient of variation (CV)0.21614245
Kurtosis4.4651496
Mean2521.3656
Median Absolute Deviation (MAD)250
Skewness-1.3192166
Sum2861750
Variance296996.81
MonotonicityNot monotonic
2024-04-05T21:45:24.349445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2750 392
34.5%
2000 353
31.1%
3000 225
19.8%
2500 68
 
6.0%
3500 52
 
4.6%
1750 29
 
2.6%
0 16
 
1.4%
ValueCountFrequency (%)
0 16
 
1.4%
1750 29
 
2.6%
2000 353
31.1%
2500 68
 
6.0%
2750 392
34.5%
3000 225
19.8%
3500 52
 
4.6%
ValueCountFrequency (%)
3500 52
 
4.6%
3000 225
19.8%
2750 392
34.5%
2500 68
 
6.0%
2000 353
31.1%
1750 29
 
2.6%
0 16
 
1.4%

Service address
Text

MISSING 

Distinct73
Distinct (%)6.5%
Missing16
Missing (%)1.4%
Memory size9.0 KiB
2024-04-05T21:45:24.797719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length66
Median length38
Mean length24.043789
Min length5

Characters and Unicode

Total characters26905
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st rowZango Fase 1,
2nd rowZango Fase 1,
3rd rowZango Fase 1,
4th rowZango Fase 1,
5th rowZango Fase 1,
ValueCountFrequency (%)
rua 208
 
5.0%
kilamba 186
 
4.5%
luanda 152
 
3.7%
do 145
 
3.5%
condominio 133
 
3.2%
maianga 129
 
3.1%
talatona 95
 
2.3%
casa 88
 
2.1%
patriota 74
 
1.8%
de 71
 
1.7%
Other values (144) 2852
69.0%
2024-04-05T21:45:25.617544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4297
16.0%
3013
 
11.2%
o 2119
 
7.9%
i 1791
 
6.7%
n 1749
 
6.5%
d 1187
 
4.4%
r 1075
 
4.0%
e 1028
 
3.8%
l 1020
 
3.8%
u 883
 
3.3%
Other values (60) 8743
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4297
16.0%
3013
 
11.2%
o 2119
 
7.9%
i 1791
 
6.7%
n 1749
 
6.5%
d 1187
 
4.4%
r 1075
 
4.0%
e 1028
 
3.8%
l 1020
 
3.8%
u 883
 
3.3%
Other values (60) 8743
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4297
16.0%
3013
 
11.2%
o 2119
 
7.9%
i 1791
 
6.7%
n 1749
 
6.5%
d 1187
 
4.4%
r 1075
 
4.0%
e 1028
 
3.8%
l 1020
 
3.8%
u 883
 
3.3%
Other values (60) 8743
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4297
16.0%
3013
 
11.2%
o 2119
 
7.9%
i 1791
 
6.7%
n 1749
 
6.5%
d 1187
 
4.4%
r 1075
 
4.0%
e 1028
 
3.8%
l 1020
 
3.8%
u 883
 
3.3%
Other values (60) 8743
32.5%

Service city
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.6%
Missing822
Missing (%)72.4%
Memory size9.0 KiB
Luanda
307 
luanda
 
6

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1878
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLuanda
2nd rowLuanda
3rd rowLuanda
4th rowLuanda
5th rowLuanda

Common Values

ValueCountFrequency (%)
Luanda 307
 
27.0%
luanda 6
 
0.5%
(Missing) 822
72.4%

Length

2024-04-05T21:45:25.918085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-05T21:45:26.156615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
luanda 313
100.0%

Most occurring characters

ValueCountFrequency (%)
a 626
33.3%
u 313
16.7%
n 313
16.7%
d 313
16.7%
L 307
16.3%
l 6
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 626
33.3%
u 313
16.7%
n 313
16.7%
d 313
16.7%
L 307
16.3%
l 6
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 626
33.3%
u 313
16.7%
n 313
16.7%
d 313
16.7%
L 307
16.3%
l 6
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 626
33.3%
u 313
16.7%
n 313
16.7%
d 313
16.7%
L 307
16.3%
l 6
 
0.3%

Service state
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1135
Missing (%)100.0%
Memory size9.0 KiB

Service postal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1135
Missing (%)100.0%
Memory size9.0 KiB

Service distance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.886555
Minimum0
Maximum45.7
Zeros210
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:26.712662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.16
median12.11
Q318.98
95-th percentile28.23
Maximum45.7
Range45.7
Interquartile range (IQR)14.82

Descriptive statistics

Standard deviation10.565427
Coefficient of variation (CV)0.81987988
Kurtosis0.70435247
Mean12.886555
Median Absolute Deviation (MAD)7.95
Skewness0.87355533
Sum14626.24
Variance111.62825
MonotonicityNot monotonic
2024-04-05T21:45:26.988266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 210
18.5%
8.94 137
 
12.1%
4.16 91
 
8.0%
24.61 76
 
6.7%
24.79 50
 
4.4%
26.84 35
 
3.1%
45.7 33
 
2.9%
28.23 32
 
2.8%
17 28
 
2.5%
12.11 27
 
2.4%
Other values (36) 416
36.7%
ValueCountFrequency (%)
0 210
18.5%
3.29 4
 
0.4%
4.13 7
 
0.6%
4.16 91
8.0%
4.24 9
 
0.8%
5.64 13
 
1.1%
5.9 18
 
1.6%
6.65 15
 
1.3%
8.94 137
12.1%
9.1 20
 
1.8%
ValueCountFrequency (%)
45.7 33
2.9%
28.58 17
 
1.5%
28.23 32
2.8%
26.84 35
3.1%
26.55 11
 
1.0%
26.36 4
 
0.4%
24.79 50
4.4%
24.61 76
6.7%
24.33 11
 
1.0%
20.04 8
 
0.7%

Lineitem quantity
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
1
988 
2
132 
3
 
10
5
 
3
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1135
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 988
87.0%
2 132
 
11.6%
3 10
 
0.9%
5 3
 
0.3%
4 2
 
0.2%

Length

2024-04-05T21:45:27.231965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-05T21:45:27.515647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 988
87.0%
2 132
 
11.6%
3 10
 
0.9%
5 3
 
0.3%
4 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 988
87.0%
2 132
 
11.6%
3 10
 
0.9%
5 3
 
0.3%
4 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 988
87.0%
2 132
 
11.6%
3 10
 
0.9%
5 3
 
0.3%
4 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 988
87.0%
2 132
 
11.6%
3 10
 
0.9%
5 3
 
0.3%
4 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 988
87.0%
2 132
 
11.6%
3 10
 
0.9%
5 3
 
0.3%
4 2
 
0.2%
Distinct114
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:27.963865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length34
Median length23
Mean length14.494273
Min length4

Characters and Unicode

Total characters16451
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)1.3%

Sample

1st rowAbacate (4 unidades)
2nd rowananás pequeno
3rd rowbanana grande
4th rowbanana pão (kg)
5th rowbatata rena branca (4kg)
ValueCountFrequency (%)
1kg 105
 
4.0%
kg 92
 
3.5%
batata 77
 
3.0%
de 77
 
3.0%
3kg 75
 
2.9%
monte 73
 
2.8%
banana 58
 
2.2%
1 58
 
2.2%
branca 57
 
2.2%
cebola 57
 
2.2%
Other values (142) 1874
72.0%
2024-04-05T21:45:28.807377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2119
 
12.9%
1539
 
9.4%
e 1435
 
8.7%
o 1126
 
6.8%
n 937
 
5.7%
r 739
 
4.5%
t 570
 
3.5%
i 562
 
3.4%
m 552
 
3.4%
g 534
 
3.2%
Other values (54) 6338
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2119
 
12.9%
1539
 
9.4%
e 1435
 
8.7%
o 1126
 
6.8%
n 937
 
5.7%
r 739
 
4.5%
t 570
 
3.5%
i 562
 
3.4%
m 552
 
3.4%
g 534
 
3.2%
Other values (54) 6338
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2119
 
12.9%
1539
 
9.4%
e 1435
 
8.7%
o 1126
 
6.8%
n 937
 
5.7%
r 739
 
4.5%
t 570
 
3.5%
i 562
 
3.4%
m 552
 
3.4%
g 534
 
3.2%
Other values (54) 6338
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2119
 
12.9%
1539
 
9.4%
e 1435
 
8.7%
o 1126
 
6.8%
n 937
 
5.7%
r 739
 
4.5%
t 570
 
3.5%
i 562
 
3.4%
m 552
 
3.4%
g 534
 
3.2%
Other values (54) 6338
38.5%

Lineitem sku
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1135
Missing (%)100.0%
Memory size9.0 KiB

Lineitem price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct70
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2913.0308
Minimum850
Maximum116850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:29.122923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum850
5-th percentile1200
Q11650
median2550
Q33680
95-th percentile5500
Maximum116850
Range116000
Interquartile range (IQR)2030

Descriptive statistics

Standard deviation4023.5961
Coefficient of variation (CV)1.3812405
Kurtosis596.4088
Mean2913.0308
Median Absolute Deviation (MAD)900
Skewness22.28397
Sum3306290
Variance16189326
MonotonicityNot monotonic
2024-04-05T21:45:29.419124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500 90
 
7.9%
2500 74
 
6.5%
2950 64
 
5.6%
2850 63
 
5.6%
1250 61
 
5.4%
1750 58
 
5.1%
2550 41
 
3.6%
1950 41
 
3.6%
2650 40
 
3.5%
950 36
 
3.2%
Other values (60) 567
50.0%
ValueCountFrequency (%)
850 10
 
0.9%
870 3
 
0.3%
950 36
3.2%
1125 1
 
0.1%
1200 26
2.3%
1250 61
5.4%
1255 7
 
0.6%
1260 9
 
0.8%
1300 3
 
0.3%
1330 21
 
1.9%
ValueCountFrequency (%)
116850 1
 
0.1%
57150 1
 
0.1%
11626 5
 
0.4%
6750 22
1.9%
5950 5
 
0.4%
5850 17
1.5%
5500 7
 
0.6%
5200 2
 
0.2%
4950 11
1.0%
4850 2
 
0.2%

Lineitem cost
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
0
1078 
1
 
22
3
 
22
2
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1135
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1078
95.0%
1 22
 
1.9%
3 22
 
1.9%
2 13
 
1.1%

Length

2024-04-05T21:45:29.892947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-05T21:45:30.320096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1078
95.0%
1 22
 
1.9%
3 22
 
1.9%
2 13
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1078
95.0%
1 22
 
1.9%
3 22
 
1.9%
2 13
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1078
95.0%
1 22
 
1.9%
3 22
 
1.9%
2 13
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1078
95.0%
1 22
 
1.9%
3 22
 
1.9%
2 13
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1078
95.0%
1 22
 
1.9%
3 22
 
1.9%
2 13
 
1.1%

Lineitem unit value
Real number (ℝ)

Distinct12
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.430396
Minimum0
Maximum700
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:30.684459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile10
Maximum700
Range700
Interquartile range (IQR)0

Descriptive statistics

Standard deviation91.061797
Coefficient of variation (CV)5.2243101
Kurtosis31.275721
Mean17.430396
Median Absolute Deviation (MAD)0
Skewness5.6744615
Sum19783.5
Variance8292.2509
MonotonicityNot monotonic
2024-04-05T21:45:31.046836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1002
88.3%
4 35
 
3.1%
500 29
 
2.6%
3 20
 
1.8%
10 19
 
1.7%
2.5 9
 
0.8%
2 6
 
0.5%
700 5
 
0.4%
30 5
 
0.4%
3.5 2
 
0.2%
Other values (2) 3
 
0.3%
ValueCountFrequency (%)
0 2
 
0.2%
1 1002
88.3%
2 6
 
0.5%
2.5 9
 
0.8%
3 20
 
1.8%
3.5 2
 
0.2%
4 35
 
3.1%
10 19
 
1.7%
30 5
 
0.4%
200 1
 
0.1%
ValueCountFrequency (%)
700 5
 
0.4%
500 29
2.6%
200 1
 
0.1%
30 5
 
0.4%
10 19
1.7%
4 35
3.1%
3.5 2
 
0.2%
3 20
1.8%
2.5 9
 
0.8%
2 6
 
0.5%

Lineitem unit
Categorical

Distinct6
Distinct (%)0.5%
Missing2
Missing (%)0.2%
Memory size9.0 KiB
QTY
584 
KG
349 
PAX
59 
PCS
 
56
PACK
 
45

Length

Max length4
Median length3
Mean length2.6610768
Min length1

Characters and Unicode

Total characters3015
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKG
2nd rowQTY
3rd rowKG
4th rowQTY
5th rowQTY

Common Values

ValueCountFrequency (%)
QTY 584
51.5%
KG 349
30.7%
PAX 59
 
5.2%
PCS 56
 
4.9%
PACK 45
 
4.0%
G 40
 
3.5%
(Missing) 2
 
0.2%

Length

2024-04-05T21:45:31.549845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-05T21:45:32.065386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
qty 584
51.5%
kg 349
30.8%
pax 59
 
5.2%
pcs 56
 
4.9%
pack 45
 
4.0%
g 40
 
3.5%

Most occurring characters

ValueCountFrequency (%)
Q 584
19.4%
T 584
19.4%
Y 584
19.4%
K 394
13.1%
G 389
12.9%
P 160
 
5.3%
A 104
 
3.4%
C 101
 
3.3%
X 59
 
2.0%
S 56
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Q 584
19.4%
T 584
19.4%
Y 584
19.4%
K 394
13.1%
G 389
12.9%
P 160
 
5.3%
A 104
 
3.4%
C 101
 
3.3%
X 59
 
2.0%
S 56
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Q 584
19.4%
T 584
19.4%
Y 584
19.4%
K 394
13.1%
G 389
12.9%
P 160
 
5.3%
A 104
 
3.4%
C 101
 
3.3%
X 59
 
2.0%
S 56
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Q 584
19.4%
T 584
19.4%
Y 584
19.4%
K 394
13.1%
G 389
12.9%
P 160
 
5.3%
A 104
 
3.4%
C 101
 
3.3%
X 59
 
2.0%
S 56
 
1.9%

Lineitem option
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1135
Missing (%)100.0%
Memory size9.0 KiB

Lineitem note
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1135
Missing (%)100.0%
Memory size9.0 KiB
Distinct78
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:32.554295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length18
Mean length13.230837
Min length4

Characters and Unicode

Total characters15017
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.4%

Sample

1st rowDomingas Almeida
2nd rowDomingas Almeida
3rd rowDomingas Almeida
4th rowDomingas Almeida
5th rowDomingas Almeida
ValueCountFrequency (%)
silva 64
 
3.0%
vanessa 43
 
2.0%
manuel 42
 
2.0%
gladys 39
 
1.8%
djamila 39
 
1.8%
morgado 39
 
1.8%
dinis 39
 
1.8%
elizângela 38
 
1.8%
brito 36
 
1.7%
sara 34
 
1.6%
Other values (125) 1735
80.8%
2024-04-05T21:45:33.272418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2176
14.5%
i 1432
 
9.5%
1203
 
8.0%
e 1180
 
7.9%
l 971
 
6.5%
r 809
 
5.4%
o 716
 
4.8%
n 692
 
4.6%
s 606
 
4.0%
u 409
 
2.7%
Other values (48) 4823
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2176
14.5%
i 1432
 
9.5%
1203
 
8.0%
e 1180
 
7.9%
l 971
 
6.5%
r 809
 
5.4%
o 716
 
4.8%
n 692
 
4.6%
s 606
 
4.0%
u 409
 
2.7%
Other values (48) 4823
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2176
14.5%
i 1432
 
9.5%
1203
 
8.0%
e 1180
 
7.9%
l 971
 
6.5%
r 809
 
5.4%
o 716
 
4.8%
n 692
 
4.6%
s 606
 
4.0%
u 409
 
2.7%
Other values (48) 4823
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2176
14.5%
i 1432
 
9.5%
1203
 
8.0%
e 1180
 
7.9%
l 971
 
6.5%
r 809
 
5.4%
o 716
 
4.8%
n 692
 
4.6%
s 606
 
4.0%
u 409
 
2.7%
Other values (48) 4823
32.1%

Customer phone
Real number (ℝ)

Distinct73
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4836361 × 1011
Minimum2.449154 × 1011
Maximum3.5193683 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-04-05T21:45:33.594031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.449154 × 1011
5-th percentile2.4492224 × 1011
Q12.449236 × 1011
median2.4492675 × 1011
Q32.4493657 × 1011
95-th percentile2.4494997 × 1011
Maximum3.5193683 × 1011
Range1.0702143 × 1011
Interquartile range (IQR)12970439

Descriptive statistics

Standard deviation1.8331069 × 1010
Coefficient of variation (CV)0.073807389
Kurtosis27.712752
Mean2.4836361 × 1011
Median Absolute Deviation (MAD)3448735
Skewness5.4188113
Sum2.8189269 × 1014
Variance3.3602811 × 1020
MonotonicityNot monotonic
2024-04-05T21:45:33.903760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.449265059 × 101140
 
3.5%
2.449275564 × 101139
 
3.4%
2.449242575 × 101139
 
3.4%
2.449233367 × 101138
 
3.3%
2.449267496 × 101132
 
2.8%
2.44923453 × 101131
 
2.7%
2.449336751 × 101131
 
2.7%
2.449236146 × 101128
 
2.5%
2.449284169 × 101127
 
2.4%
2.449239611 × 101127
 
2.4%
Other values (63) 803
70.7%
ValueCountFrequency (%)
2.449153984 × 101113
1.1%
2.449186487 × 101111
1.0%
2.449220921 × 101111
1.0%
2.449221541 × 101121
1.9%
2.449222374 × 101122
1.9%
2.44922332 × 101114
1.2%
2.449227488 × 10118
 
0.7%
2.44923291 × 101113
1.1%
2.449233236 × 101126
2.3%
2.449233274 × 10119
 
0.8%
ValueCountFrequency (%)
3.519368277 × 101113
1.1%
3.519276695 × 101121
1.9%
2.648578069 × 101113
1.1%
2.449499731 × 101123
2.0%
2.449494647 × 10112
 
0.2%
2.449492397 × 101115
1.3%
2.449492064 × 101114
1.2%
2.449491206 × 101121
1.9%
2.449488799 × 10111
 
0.1%
2.449456957 × 101115
1.3%

Customer email
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1135
Missing (%)100.0%
Memory size9.0 KiB

Remark
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1135
Missing (%)100.0%
Memory size9.0 KiB

Internal note
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1135
Missing (%)100.0%
Memory size9.0 KiB
Distinct86
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Minimum2024-03-19 13:46:00
Maximum2024-04-05 12:21:00
2024-04-05T21:45:34.203000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:34.490638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-04-05T21:45:12.839914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:56.803556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:59.483707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:01.563257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:03.646014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:06.509580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:08.797791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:10.813178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:13.099427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:57.045269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:59.724753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:01.812833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:03.917647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:07.049853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:09.059023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:11.070414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:13.346359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:57.323454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:59.982807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:02.094114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:04.254843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:07.288571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:09.304084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:11.326102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:13.601096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:57.568771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:00.251595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:02.354458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:04.649672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:07.528931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:09.553580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:11.579762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:13.862715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:57.819598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:00.523087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:02.634491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:05.037531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:07.783825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:09.823222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:11.845857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:14.120482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:58.057968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:00.755518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:02.878748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:05.412028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:08.010626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:10.050376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:12.085790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:14.365762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:58.308452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:01.015474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:03.137001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:05.803328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:08.271660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:10.300652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:12.336422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:14.622930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:44:59.225207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:01.272111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:03.389489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:06.148533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:08.530684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:10.556602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-05T21:45:12.590615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-04-05T21:45:34.762346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Customer phoneDateDiscountLineitem costLineitem priceLineitem quantityLineitem unitLineitem unit valueOrder numberPayment StatusService cityService distanceService feeStatusSubtotalTotal
Customer phone1.0000.2460.3850.0160.0640.0610.0000.0090.0880.1890.000-0.194-0.1910.156-0.103-0.103
Date0.2461.0000.3630.000-0.0230.2870.0000.035-0.0970.8290.356-0.046-0.1120.709-0.034-0.034
Discount0.3850.3631.0000.000-0.0430.0690.000-0.0280.1010.2600.000-0.129-0.0220.193-0.116-0.116
Lineitem cost0.0160.0000.0001.0000.1530.0930.287-0.081-0.0340.0650.000-0.046-0.0450.035-0.008-0.008
Lineitem price0.064-0.023-0.0430.1531.0000.0000.0820.163-0.0000.0281.000-0.074-0.0840.0000.0330.033
Lineitem quantity0.0610.2870.0690.0930.0001.0000.029-0.086-0.0060.0000.0000.0490.0590.0000.0230.023
Lineitem unit0.0000.0000.0000.2870.0820.0291.000-0.3800.0060.0470.177-0.069-0.0670.000-0.000-0.000
Lineitem unit value0.0090.035-0.028-0.0810.163-0.086-0.3801.000-0.0290.0000.000-0.006-0.0070.0000.0450.045
Order number0.088-0.0970.101-0.034-0.000-0.0060.006-0.0291.0000.8090.1510.1270.2470.677-0.112-0.112
Payment Status0.1890.8290.2600.0650.0280.0000.0470.0000.8091.0000.1840.1930.3320.928-0.141-0.141
Service city0.0000.3560.0000.0001.0000.0000.1770.0000.1510.1841.0000.0290.0780.010-0.230-0.230
Service distance-0.194-0.046-0.129-0.046-0.0740.049-0.069-0.0060.1270.1930.0291.0000.9140.3350.0020.002
Service fee-0.191-0.112-0.022-0.045-0.0840.059-0.067-0.0070.2470.3320.0780.9141.0000.422-0.040-0.040
Status0.1560.7090.1930.0350.0000.0000.0000.0000.6770.9280.0100.3350.4221.000-0.113-0.113
Subtotal-0.103-0.034-0.116-0.0080.0330.023-0.0000.045-0.112-0.141-0.2300.002-0.040-0.1131.0001.000
Total-0.103-0.034-0.116-0.0080.0330.023-0.0000.045-0.112-0.141-0.2300.002-0.040-0.1131.0001.000

Missing values

2024-04-05T21:45:15.343413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-05T21:45:16.268211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-05T21:45:16.803555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateOrder numberOrder nameStatusPayment StatusSubtotalTaxTotalDiscountDiscount detailsService nameService dateService timeService feeService addressService cityService stateService postalService distanceLineitem quantityLineitem nameLineitem skuLineitem priceLineitem costLineitem unit valueLineitem unitLineitem optionLineitem noteCustomer nameCustomer phoneCustomer emailRemarkInternal noteCreated at
005 Apr 2024117#1172024PendingUnpaid75350.0075350.00NaNTaxa de entregaNaNNaN3000Zango Fase 1,NaNNaNNaN45.71Abacate (4 unidades)NaN2500.001.0KGNaNNaNDomingas Almeida244941495954NaNNaNNaN2024-04-05 12:21
105 Apr 2024117#1172024PendingUnpaid75350.0075350.00NaNTaxa de entregaNaNNaN3000Zango Fase 1,NaNNaNNaN45.72ananás pequenoNaN2750.011.0QTYNaNNaNDomingas Almeida244941495954NaNNaNNaN2024-04-05 12:21
205 Apr 2024117#1172024PendingUnpaid75350.0075350.00NaNTaxa de entregaNaNNaN3000Zango Fase 1,NaNNaNNaN45.71banana grandeNaN1250.001.0KGNaNNaNDomingas Almeida244941495954NaNNaNNaN2024-04-05 12:21
305 Apr 2024117#1172024PendingUnpaid75350.0075350.00NaNTaxa de entregaNaNNaN3000Zango Fase 1,NaNNaNNaN45.71banana pão (kg)NaN2550.001.0QTYNaNNaNDomingas Almeida244941495954NaNNaNNaN2024-04-05 12:21
405 Apr 2024117#1172024PendingUnpaid75350.0075350.00NaNTaxa de entregaNaNNaN3000Zango Fase 1,NaNNaNNaN45.71batata rena branca (4kg)NaN4750.001.0QTYNaNNaNDomingas Almeida244941495954NaNNaNNaN2024-04-05 12:21
505 Apr 2024117#1172024PendingUnpaid75350.0075350.00NaNTaxa de entregaNaNNaN3000Zango Fase 1,NaNNaNNaN45.71batata rena vermelha (4kg)NaN5850.001.0QTYNaNNaNDomingas Almeida244941495954NaNNaNNaN2024-04-05 12:21
605 Apr 2024117#1172024PendingUnpaid75350.0075350.00NaNTaxa de entregaNaNNaN3000Zango Fase 1,NaNNaNNaN45.71cebola branca (3kg)NaN5850.001.0QTYNaNNaNDomingas Almeida244941495954NaNNaNNaN2024-04-05 12:21
705 Apr 2024117#1172024PendingUnpaid75350.0075350.00NaNTaxa de entregaNaNNaN3000Zango Fase 1,NaNNaNNaN45.71Cenoura (2,5kg)NaN4750.001.0QTYNaNNaNDomingas Almeida244941495954NaNNaNNaN2024-04-05 12:21
805 Apr 2024117#1172024PendingUnpaid75350.0075350.00NaNTaxa de entregaNaNNaN3000Zango Fase 1,NaNNaNNaN45.71Chá de camomilaNaN1750.001.0QTYNaNNaNDomingas Almeida244941495954NaNNaNNaN2024-04-05 12:21
905 Apr 2024117#1172024PendingUnpaid75350.0075350.00NaNTaxa de entregaNaNNaN3000Zango Fase 1,NaNNaNNaN45.72Feijão catarino (1kg)NaN2550.001.0QTYNaNNaNDomingas Almeida244941495954NaNNaNNaN2024-04-05 12:21
DateOrder numberOrder nameStatusPayment StatusSubtotalTaxTotalDiscountDiscount detailsService nameService dateService timeService feeService addressService cityService stateService postalService distanceLineitem quantityLineitem nameLineitem skuLineitem priceLineitem costLineitem unit valueLineitem unitLineitem optionLineitem noteCustomer nameCustomer phoneCustomer emailRemarkInternal noteCreated at
112520 Mar 202418#182024FulfilledPaid25425.0025425.00NaNTaxa de entregaNaNNaN2000Maianga, mutambaNaNNaNNaN8.941alface roxo (monte)NaN3825.001.0QTYNaNNaNCleida Napoleao244925947490NaNNaNNaN2024-03-20 15:57
112620 Mar 202418#182024FulfilledPaid25425.0025425.00NaNTaxa de entregaNaNNaN2000Maianga, mutambaNaNNaNNaN8.941banana pão (kg)NaN2550.001.0QTYNaNNaNCleida Napoleao244925947490NaNNaNNaN2024-03-20 15:57
112720 Mar 202418#182024FulfilledPaid25425.0025425.00NaNTaxa de entregaNaNNaN2000Maianga, mutambaNaNNaNNaN8.941cebola branca (3kg)NaN4500.001.0QTYNaNNaNCleida Napoleao244925947490NaNNaNNaN2024-03-20 15:57
112820 Mar 202418#182024FulfilledPaid25425.0025425.00NaNTaxa de entregaNaNNaN2000Maianga, mutambaNaNNaNNaN8.941MaracujaNaN2850.001.0KGNaNNaNCleida Napoleao244925947490NaNNaNNaN2024-03-20 15:57
112920 Mar 202418#182024FulfilledPaid25425.0025425.00NaNTaxa de entregaNaNNaN2000Maianga, mutambaNaNNaNNaN8.942pitayaNaN1950.001.0KGNaNNaNCleida Napoleao244925947490NaNNaNNaN2024-03-20 15:57
113020 Mar 202418#182024FulfilledPaid25425.0025425.00NaNTaxa de entregaNaNNaN2000Maianga, mutambaNaNNaNNaN8.941Repolho brancoNaN1350.001.0KGNaNNaNCleida Napoleao244925947490NaNNaNNaN2024-03-20 15:57
113120 Mar 202418#182024FulfilledPaid25425.0025425.00NaNTaxa de entregaNaNNaN2000Maianga, mutambaNaNNaNNaN8.941chá de caxinde (1 monte)NaN950.001.0QTYNaNNaNCleida Napoleao244925947490NaNNaNNaN2024-03-20 15:57
113220 Mar 202418#182024FulfilledPaid25425.0025425.00NaNTaxa de entregaNaNNaN2000Maianga, mutambaNaNNaNNaN8.941ananás (1uni)NaN1750.000.0NaNNaNNaNCleida Napoleao244925947490NaNNaNNaN2024-03-20 15:57
113320 Mar 202418#182024FulfilledPaid25425.0025425.00NaNTaxa de entregaNaNNaN2000Maianga, mutambaNaNNaNNaN8.941Couve (1kg)NaN1750.001.0QTYNaNNaNCleida Napoleao244925947490NaNNaNNaN2024-03-20 15:57
113419 Mar 202410#102024FulfilledPaid25250.0025250.00NaNTaxa de entregaNaNNaN2000Nova VidaNaNNaNNaN6.655batata rena vermelha (4kg)NaN4650.001.0QTYNaNNaNPaula dos Santos244941455368NaNNaNNaN2024-03-19 13:46